Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
In the response filed on 14 December 2025, the following has occurred: claims 1, 6-9, 16 and 20 have been amended.
Now claims 1-10, 12-18 and 20-22 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10, 12-18 and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 16 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite computer implemented method, system and non-transitory computer-readable storage medium (CRM) for performing the limitations of:
Claim 1, which is representative of claims 16 and 20
[…] improving accuracy of food weight measurement […], the method comprising: [… collect …] a plurality of food intake events of a user occurred at a plurality of venues, wherein the obtaining comprises: collecting first food intake events of the […], wherein the collecting comprises: continuously collecting weight readings […] to measure weight changes of a food container at millisecond intervals; obtaining a plurality of 3D point cloud images with depth information capturing hand motions of the user; feeding, in real time, the plurality of 3D point cloud images into a [… model …] for identifying a user action based on the hand motions, wherein the user action comprises lifting a serving utensil, taking food using the serving utensil from the food container, dropping food back to the food container, stirring food, or placing the serving utensil back to the food container; determining, in real time, […], the user action associated with a time window of weight fluctuation in the weight readings, wherein the time window is between a start and a completion of the user action; determining, in real time, based on the determined user action, whether the weight fluctuation during the time window are attributable to food removal or to a non-serving event including at least one of stirring food, fluid sloshing, utensil movement, or environmental vibration that causes transient weight fluctuation; adjusting, in real time, the weight readings […] to compensate the weight fluctuation based on the determined user action, wherein the adjusting automatically compensates for the transient weight fluctuations caused by the non-serving event, thereby improving accuracy of weight measurements in the presence of transient physical disturbances; determining, in real time, an amount of food taken by the user based on the adjusted weight readings; determining, in real time, portion-based dietary information at least based on the amount of food taken by the user; and associating the portion-based dietary information of the first food intake event with an identification of the user to form a first food intake event; [… saving …] the plurality of food intake events based on the identification of the user associated with the plurality of food intake events; and automatically generating a dietary analysis report for the user based on the plurality of food intake events of the user collected from the plurality of venues; and [… providing …] the dietary analysis report to […] the user such that the user has access to an end-to-end coverage of dietary features of the user, the method further comprising [… creating a model …] based on visual data collected […] for user action recognition, wherein the visual data comprises hand motions or trajectories of a given user with depth information and labeled user action, and the [… creating …] comprises: computing a distance between predicted user action […] and the labeled user action; and [… updating the model …] to minimize the distance in subsequent training.
, as drafted, is a method, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). That is by a human user interacting with a computer with an Internet of things (IoT) system, weight sensors, a 3D camera, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, weight sensors, a 3D camera, a device (claims 16 and 20), the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, by a human interacting with the a computer with an Internet of things (IoT) system, weight sensors, a 3D camera, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, weight sensors, a 3D camera, a device (claims 16 and 20), the claim encompasses a user logging and monitoring a plurality of a dietary intake events, organizing the collected data with a model to determine features and determine user consumption to generate and provide a user a dietary report for a human user to use. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computer with an Internet of things (IoT) system, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, a device (claims 16 and 20), which implements the abstract idea. The an Internet of things (IoT) system, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, a device (claims 16 and 20) an Internet of things (IoT) system, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, a device (claims 16 and 20) are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 7, paragraphs [0129]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of weight sensors, a 3D camera, “obtaining… transmitting…”, “using the channel-separated neural network”, “storing…” and “training the channel-separated neural network… the training comprises… adjusting weights of the channel-separated neural network” to implement the abstract idea. The weight sensors are recited at a high level of generality (i.e., a generic off the shelf sensor/scale attached to a generic container) and amounts to generally linking the abstract idea to a particular technological environment. The 3D camera is recited at a high level of generality (i.e., a generic off the shelf 3d camera) and amounts to generally linking the abstract idea to a particular technological environment. The “obtaining… transmitting…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “using the channel-separated neural network” steps are recited at a high-level of generality (i.e., using a generic off-the shelf model) and amounts to generally linking the abstract idea to a particular technological environment. The “storing…” is recited at a high-level of generality (i.e., as a general means of storing data) and amounts to the mere storage of data, which is a form of extra-solution activity. The “training the channel-separated neural network… the training comprises… adjusting weights of the channel-separated neural network” are recited at a high-level of generality (i.e., training and using a generic off-the shelf neural network that is updated) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer with an Internet of things (IoT) system, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, a device (claims 16 and 20) to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of weight sensors, a 3D camera, “obtaining… transmitting…”, “using the channel-separated neural network”, “storing…” and ““training the channel-separated neural network… the training comprises… adjusting weights of the channel-separated neural network” were considered generally linking the abstract idea to particular technological environment and/or extra-solution activity. The weight sensors have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): Figure 1, paragraphs [0047]-[0048]; Kim (2020/0365250): Figure 1, paragraphs [0007], [0014], [0094]; use of sensors to capture weight of a food container are well-understood, routine and conventional. The 3D camera has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): Figure 1, paragraphs [0047]-[0048]; Kim (2020/0365250): Figure 1, paragraphs [0014], [0094]; use of a camera to capture image of food is well-understood, routine and conventional. The “obtaining… transmitting…” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The “using the channel-separated neural network” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): paragraphs [0014]; Kim (2020/0365250): paragraph [0113]; use of an off the shelf-machine learning model is well-understood, routine and conventional. The “storing…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(iv) “Storing and retrieving information in memory” is well-understood, routine, and conventional. The “training the channel-separated neural network… the training comprises… adjusting weights of the channel-separated neural network” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Guo (20200356842): paragraphs [0006], [0027], [0049]-[0052]; Armstrong (20200193620): Figures 5-6, paragraphs [0017], [0040], [0043]-[0045]; Denli (20190064389): Figure 2, paragraphs [0019], [0056], [0058]-[0062]; Chen (20210067527): Figure 2, paragraphs [0065]-[0066], [0091]-[0094]; Jha (20220374292): Figures 28, 30-31, paragraph [0035], [0089] and [0104]; use of a channel-separated neural network that is trained by adjusting weights is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-10, 12-15, 17-18 and 20-22 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2-3, 12, 15 and 17-18 recite the additional element of “displaying, on a display”, however this is recited at a high-level of generality (i.e., as a generic presentation of information to a user) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “displaying, on a display” was considered generally linking the abstract idea to particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): paragraphs [0064]; Kim (2020/0365250): paragraph [0020]; Ou (2018/0114601): paragraph [0253]; displaying information on a display is Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claim 4 further describes updating of information, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 5 and 6 further describe association and creation of lists, however does not recite any additional elements not already considered above are therefore cannot provide a practical application and/or significantly more.
Claims 7 and 8 recite the additional elements of “clustering, using unsupervised learning” and “training, using supervised training”, however these are recited at a high level of generality (i.e., generic off-the shelf machine learning techniques) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “clustering, using unsupervised learning” and “training, using supervised training” were considered generally linking the abstract idea to particular technological environment. The “clustering, using unsupervised learning” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Malecha (2023/0060235): paragraph [0037]; Lee (20180157936): paragraph [0100]; Simpson (20210335499): paragraph [0135]-[0136]; use of unsupervised learning to cluster data is Well-understood, routine, and conventional elements. The “training, using supervised training” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Malecha (2023/0060235): paragraph [0165]; Lee (20180157936): paragraph [0100]; Simpson (20210335499): paragraph [0135]-[0136]; use of supervised learning to classify data is Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 9 and 10 further describe goal and feature determination, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 12 and 14 describe a machine learning model, however use of machine learning models was already considered above and is incorporated herein.
Claim 13 further describes detecting a behavior group for the report, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 21 and 22 recite the additional element of electronic appliance comprising a scale coupled with one or more weight sensors and a first camera, however this is recited at a high level of generality (i.e., a generic off the shelf camera and scale) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements were considered generally linking the abstract idea to particular technological environment. This been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): Figure 1, paragraphs [0047]-[0048]; Kim (2020/0365250): Figure 1, paragraphs [0014], [0094]; use of an appliance with a scale and camera to capture image of food and measure the weight is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Response to Arguments
Applicant's arguments filed on 14 December 2025 have been fully considered but they are not persuasive. Applicant's arguments will be addressed below in the order in which they appear in the response filed on 14 December 2025.
Rejection under 35 U.S.C. § 101
Regarding the rejection of claim 1-10, 12-18 and 20-22, the Examiner has considered the Applicant's arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
As amended, independent claim 1 is directed to a specific, technology-focused method for improving, in real-time, the accuracy of food weight measurement in an IoT food service system by fusing high-frequency load-cell signals with 3D hand-motion data processed by a channel-separated neural network, in order to distinguish actual serving events from transient non-serving disturbances (sloshing, stirring, utensil movement, vibration) and automatically correct the sensor output… Claim 1 is not directed to a method of organizing human activity or a mental process… The claim does not prescribe any rules for how a person should behave, what they should eat, or how they should manage their diet. Instead, it specifies how an IoT measurement system should operate on physical sensor data to obtain a more accurate physical measurement… The claim therefore does not fall within the "certain methods of organizing human activity" grouping, nor the "mental process" grouping… The training limitation does not recite a judicial exception and, in any event, is part of the technological improvement… The training limitation does not recite a judicial exception and, in any event, is part of the technological improvement… However, the Office characterizes this and related limitations as merely "using a generic off-the shelf neural network that is updated" and treats them as part of the alleged abstract idea… The memorandum specifically discusses Example 39… In amended claim 1, the training step is not an afterthought; it is part of the technical solution… Thus, the training limitation is not just "apply machine learning to the data"; it recites how the neural network is trained in a specific way (using specific training data and labels) so that its outputs can be used in a specific way to improve the operation of the IoT weight measurement system in a noisy physical environment… The claimed method and system address a specific technical problem that arises in the context of real-time weight-sensor-based food measurement systems: inaccurate weight measurements caused by transient, non-food-related disturbances by solely relying on weight sensors, such as the utensil movements (e.g., lifting or placing back the serving utensil) or fluid instability (e.g., sloshing soups or stews)… Human cannot accurately measure and compensate the weight fluctuation by just observing or using pen and paper, in real-time… the technical problem explicitly articulated in the specification is: how to accurately distinguish actual food-removal events from transient, non-serving weight fluctuations in real-time sensor data… The neural network is not used in some abstract, generic fashion, but in a concrete, claimed way to correct noisy physical sensor signals in real time in order to improve the functioning of the IoT food service system as a measurement device… Applicant does not dispute that load cells, cameras, and neural networks per se were known. However, the correct analysis under Step 2B is whether the ordered combination of elements, as recited in the claim, is well-understood, routine, and conventional… The references cited by the Examiner for "well-understood, routine, and conventional" use of sensors, cameras, and neural networks merely show that each type of component exists and can be used for generic measurement or classification. They do not show the specific fusion architecture and control logic recited in claim 1.
The Examiner respectfully disagrees.
It is respectfully submitted, the claims under the broadest reasonable interpretation amount to organization of the activity between a human user and various generic off-the-shelf computer components to perform dietary tracking for creation of dietary analysis report for a human user to interact with, which as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. The claim is directed toward the certain methods of organizing human activity grouping of abstract ideas under the broadest reasonable interpretation.
The claimed additional elements do not recite a technical solution to a technical problem recited in Applicant’s specification and/or an improvement in the functionality of the computer. In particular, training and utilization of the neural network are considered additional elements, however unlike example 39, the claims do not recite a technical solution to a technical problem recited in Applicant’s specification. Applicant only argues paragraphs [0060] and [0064], looking at the argued paragraphs, the paragraphs do not recite any technical problems rooted in computer hardware technology, instead at best they describe a human activity problem of capture of dietary information, which is not a technical problem rooted in computer hardware technology. The problem of “how to accurately distinguish actual food-removal events”, is not a technical problem that is rooted in computer hardware technology, at best this is a human activity problem of dietary tracking for creating a report for a human user. The determination of what the user consumes is a human activity solution to a human activity problem, which may improve upon the abstract idea, however an improved abstract idea is still an abstract idea.
The claimed additional elements are not being improved themselves (i.e., improvements in the functionality of the computer and/or technical solutions to technical problems recited in Applicant’s specification), the weight sensor, 3D camera and the training and utilization of the neural network are used as tools to collect and organize dietary consumption of a human user to provide a report for the human user, which may improve upon the abstract idea of dietary tracking, nevertheless an improved abstract idea is still an abstract idea. The additional elements even in combination amount to using generic well-understood, routine and conventional elements to apply the abstract idea to a particular technological environment. Therefore, as the claims as currently drafted do not recite a technical solution to a technical problem recited in the specification and/or an improvement in the functionality of computer Applicant’s argument is unpersuasive.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew E Lee whose telephone number is (571)272-8323. The examiner can normally be reached M-Th 9-5:00 PM.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684